



'''simple docstring'''

import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  ['''small''', '''medium''', '''large''']

SCREAMING_SNAKE_CASE__     :						int	=  '''lm_head.decoder.weight'''
SCREAMING_SNAKE_CASE__     :						int	=  '''lm_head.weight'''






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    str			)       ->	int:
		snake_case__	     =torch.load(UpperCamelCase_			)
		snake_case__	     =d.pop(UpperCamelCase_			)
		os.makedirs(UpperCamelCase_      ,					exist_ok=UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					os.path.join(UpperCamelCase_      ,					UpperCamelCase_			)			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Optional[int]	=  argparse.ArgumentParser()
			parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
			SCREAMING_SNAKE_CASE__     :						List[str]	=  parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						SCREAMING_SNAKE_CASE__     :						Tuple	=  os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
						SCREAMING_SNAKE_CASE__     :						List[str]	=  f"""./DialoGPT-{MODEL}"""
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




